Oxidation wear monitoring based on the color extraction of on-linewear debris

Yeping Peng a,b, Tonghai Wu a,n, Shuo Wang a, Zhongxiao Peng b

a Key Laboratory of Modern Design and Rotor Bearing System of Ministry, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, People's Republic of Chinab School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, NSW 2052, Australia

a r t i c l e i n f o

Article history:Received 14 September 2014Received in revised form15 December 2014Accepted 29 December 2014Available online 5 January 2015

Keywords:Oxidation wearOn-line monitoringColor extractionWear debris

a b s t r a c t

Oxidation associated wear usually involves high temperature and often accelerates lubricationdegradation and failure processes. The color of oxide wear debris highly corresponds with the severitiesof oxidation wear. Therefore, on-line detection of oxide wear debris has the advantage of revealing thewear condition in a timely manner. This paper presents a color extraction method of wear debris for on-line oxidation monitoring. Images of moving wear particles in lubricant were captured via an on-lineimaging system. Image preprocessing methods were adopted to separate wear particles from thebackground and to improve the image quality through a motion-blurred restoration process before thecolors of the wear debris were extracted. By doing this, two typical types of oxide wear debris, red Fe2O3

and black Fe3O4, were identified. Furthermore, a statistical clustering model was established forautomatic determination of the two typical types of oxide wear particles. Finally, the effectiveness ofthe proposed method was verified by performing real-time oxidation wear monitoring of experimentaldata. The proposed method provides a feasible approach to detect early oxidation wear and monitor itsprogress in a running machine.

& 2015 Elsevier B.V. All rights reserved.

1. Introduction

Metallic oxidation, often caused by high temperature at metal-to-metal contacts due to overload and/or lack of lubrication, is oneof the main causes of tribological failures. High flash temperaturegenerated in the wear process will not only tender the tribomaterials but also deteriorate the lubricant in use. Therefore, earlydetection of oxidation wear is of significance for preventive main-tenance of mechanical systems [1,2].

Wear debris is the direct by-product generated in a wearprocess and contains valuable information about machine condi-tions. Thus the features of wear debris are characterized formachine condition monitoring and fault diagnosis. Among thesefeatures, the colors of wear debris are used as critical informationfor oxide wear debris identification. For instance, the predominantoxide products of most tribo-systems made of steel (Fe–C alloy)are Fe2O3 and Fe3O4, which can be identified by examining theirspecial colors. In addition, the oxide films of low alloy steel, frominside to outside, are FeO (FeO cannot be produced under 600 1C),Fe3O4 and Fe2O3 layer [3]. If the oxidation film breaks up to formwear debris, it means that severe wear with a high wear rateoccurs and the types of oxide wear particles can reveal the wear

degree. At present, color-based classification of oxide wear debrishas been studied extensively and accepted as a theoretical base foroxidation wear analysis [4,5].

Color extraction of wear debris is an essential step for oxidationwear monitoring using off-line analysis techniques. Myshkin [4]used optical microscopy to acquire wear debris images and extracttheir colors. By using a multi-scale classification criterion, wearparticles were classified into red oxide (Fe2O3) and black oxide(Fe3O4) based on their colors. Compared with off-line approachessuch as Myshkin's work, on-line monitoring technology has theadvantage of capturing real-time machine data [6,7]. Color imagesof wear debris can also be captured using on-line ferrographtechnology to identify wear sources. Identification of some typicalmetal particles, including copper, iron and aluminum debris wasachieved through extracting their colors from on-line images [8].However, this method is effective only when wear debris imagesare clear. Unfortunately, wear debris images sampled with anonline sensor are often fuzzy because of a number of reasons, e.g.the movements of the particles and lubricant. Also, reported on-line monitoring systems applied the ferrograph approach tocapture iron particles [9]. Consequently, the particles are oftenin a chain pattern and overlapping, which make it difficult toseparate wear particles. Color recognition of moving wear debristhus remains a bottleneck for on-line wear monitoring.

Video-based image processing technology provides a newapproach for dynamic characteristics extraction, and has been

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http://dx.doi.org/10.1016/j.wear.2014.12.0470043-1648/& 2015 Elsevier B.V. All rights reserved.

n Corresponding author. Tel.: þ86 29 82669161.E-mail address: wt-h@163.com (T. Wu).

Wear 332-333 (2015) 1151–1157

successfully applied in medical diagnosis [10], robot vision [11]and intelligence transportation [12]. Specifically, video acquisitioncan avoid the agglomeration of motion particles to achieveaccurate characteristics extraction of multiple wear particles.Although the motion of wear debris introduces image blurring,color information can be obtained using color image enhancementmethods [13,14].

Aiming at on-line oxidation wear monitoring, this work inves-tigated the color extraction of wear debris from on-line sampledimages. An image capturing system was constructed to acquire thecolor images of moving wear particles. To extract the colors ofwear debris effectively, the methods of improving on-line imagesquality were studied, the color information was extracted, andwear particles were classified based on their colors for on-lineoxidation wear monitoring. The practicability of the proposedmethod was evaluated experimentally using a four-ball weartester.

2. Dynamic wear debris image acquisition system

An experimental system for oxidation wear testing and mon-itoring was designed and is illustrated in Fig. 1. A typical four-ballwear tester was adopted as a testing machine that the on-linemonitoring equipment was set up on. Particles produced by thefriction pair flew into the oil tank and then were directed to thedesigned oil flow path by a digital pump. Videos of dynamic wear

debris were captured using a video sensor with light-emittingdiodes (LED). A single chip microcomputer was utilized to drivethe digital pump and the LED. To avoid the circulation of analyzedparticles in the oil system, an oil bottle was set up in the returnpipe. The clean oil in the upper portion of the oil bottle wasdrained by another digital pump into the oil tank. The return oilwith wear debris was sent to the bottom of the oil bottle so theparticles could be deposited.

The videos of dynamic particles were processed to extractparticle images. A typical wear debris image extracted from thevideo stream is shown in Fig. 2(a). As can be seen, wear particles indifferent colors were captured. By comparing with the analyticalferrograph image in Fig. 2(b), two drawbacks of the on-line imageare revealed.

1) The background varies with light conditions and oil transpar-ency, resulting in a low contrast between the particles andbackground.

2) The resolution of the on-line image is less than that of an off-line ferrograph image, which is caused by the movement of theparticles and lubricant involvement.

These issues make it difficult to extract the color information ofthe wear debris. In order to effectively extract the information, thebackground of the image needs to be segmented and the imagequality needs to be improved. Details of the wear debris imagesegmentation and enhancement methods will be presented in thenext section.

3. Color extraction of dynamic wear debris

Images of moving particles captured by the video acquisitionsystem contain their color features for particle identification. How-ever, it is difficult to extract color information from the dynamicimage because of the two issues described in Section 2. Thus imagesegmentation and color enhancement are necessary before acquir-ing useful color information.

3.1. Color segmentation of wear debris image

As depicted in Fig. 2, a particle image can be divided into twoparts: wear debris and background. For image segmentation, abackground subtraction method [15] was employed to separatethe wear debris. A series of images were extracted from a videoclip to reconstruct a real-time background of the images. Fig. 3gives nine frames selected from the images sampled in 12 s. The

Fig. 1. Schematic illustration of the on-line monitoring equipment installed on afour-ball rig.

Fig. 2. Typical images carried by on-line monitoring equipment and analytical ferrography: (a) on-line acquisition image and (b) analytical ferrograph image.

Y. Peng et al. / Wear 332-333 (2015) 1151–11571152

Gaussian background modeling method [16] was adopted to recon-struct the background using those image frames (600 frames) aftergray processing. An example of a reconstructed background image isshown in Fig. 4.

Fig. 4 is a background image after the wear debris and artificialcolors shown in Fig. 3 were removed. The difference betweenFig. 2(a) and Fig. 4 was computed using the background subtrac-tion method mentioned above to acquire a color image of weardebris, and the resultant image is shown in Fig. 5. As can be seen,the wear debris was well segmented from the background. How-ever, the low resolution issue still needs to be resolved.

3.2. Enhancement of on-line wear debris images

A number of filters including Wiener filter [17] and medianfilter [18] were proposed for color image enhancement. As statedbefore, the dynamic particle images appear blurring mainly causedby the motion of particles and random noise. Considering thatrandom noise needs to be removed and that the Wiener filteringcan minimize the mean square error between restored and sharpimages, it was adopted as the filtering method in this work.

Fig. 6 shows the degradation model of a blurred image. Asillustrated in the degradation model, a degraded image represented

using g(x, y) results from the convolution between a sharp imagef(x, y) and a degradation function h(x, y) and is also affected by noisen(x, y).

Fig. 3. Some image frames extracted from wear debris video: (a) 50 frame, (b) 100 frame, (c) 150 frame, (d) 200 frame, (e) 250 frame, (f) 300 frame, (g) 350 frame, (h) 400frame and (i) 450 frame.

Fig. 4. The reconstructed background image based on the wear debris imagesshown in Fig. 3.

Y. Peng et al. / Wear 332-333 (2015) 1151–1157 1153

The relationship between blurred and sharp images can bedescribed as

g x; yð Þ ¼ f x; yð Þnh x; yð Þþn x; yð Þ ð1Þwhere n denotes the linear convolution.

Image restoration is the inverse operation of image degradation.The Wiener filtering method has superior performance in denoising[19], but the degradation function h(x, y) still needs to be performedto check the image restoration outcome. The relative motion ofparticles to the video sensor is the main reason for image blurring.The h(x, y) for motion blur can be expressed by [17]

h x; yð Þ ¼ 1=L;ffiffiffiffiffiffiffiffiffiffiffiffiffiffix2þy2

prL=2; y=x¼ tan θ

0; else

(ð2Þ

where L is the length of blur; θ is the angle of blur.Consequently, the parameters estimation of the blur angle θ

and length L is of significance for motion-blurred image restora-tion. Generally, the wear debris motion can be simplified asuniform linear motion at a constant velocity v and a blur directionθ when the exposure time is short. During the exposure time

interval [0, T], the blur length can be calculated using L¼ vT . Fig. 2(a) shows sampled in the conditions that the average speed ofwear debris was 2.4 mm/s and the video sampling rate was 50frames per second (fps) with a resolution of 640�480 pixels.Considering the scale factor of 1.5 μm per pixel, the length of blur Lwas calculated to be 32 pixels.

In addition, Fourier spectrum has been widely used to obtainthe motion-blurred angle of an image [20]. Parallel stripes will befound in the frequency spectrum image if there is motion blurreddirection. With 901 clockwise rotation of the angle between theparallel stripes and horizontal axis, the blur angle θ can beconfirmed. Based on this theory, the Fourier transform result ofthe dynamic wear debris image (Fig. 7(a)) is shown in Fig. 7(b). Ascan be seen, nearly no parallel stripes can be found in the middleof the spectrum image. This is because the direction of the particlemovement is parallel to the video sensor as shown in Fig. 1. Thusthe motion direction of the wear debris can be regarded ashorizontal direction, namely θ¼ 0, in the short exposure interval.

The motion-blurred image of dynamic wear debris was restoredby utilizing the Wiener filtering method with the blur angle θ¼0

Fig. 5. Wear debris image after excluding the background (Fig. 4) from the initialon-line image shown in Fig. 2(a).

Fig. 6. The degradation model of motion-blurred.

Fig. 7. Fourier transform result of a wear debris image: (a) dynamic wear debris image and (b) its Fourier spectrum image.

Fig. 8. The segmentation and restoration image of Fig. 7(a).

Table 1Results of H and I extraction of the wear debris in Fig. 8.

Debris number 1 2 3 4 5 6 7 8 9

H /degree 0.66 0.74 0.50 0.90 0.73 0.60 0.18 0.02 0.13I /dimensionless 0.38 0.28 0.30 0.06 0.13 0.19 0.28 0.36 0.14Particles color other other other black black black red red red

Y. Peng et al. / Wear 332-333 (2015) 1151–11571154

and the blur length L¼32 pixels, and the result is shown in Fig. 8.Clearer particles are displayed in the restoration image than thosein Fig. 5. The processed image is appropriate for wear debris color

extraction. It is worth noting that the image separation andrestoration process does not alter the color information of theparticles.

3.3. Color extraction of wear debris

A color image can be expressed by twomodels including red, greenand blue (RGB) color model and hue-saturation-intensity (HSI) model.R, G and B denote the pixels in the corresponding color channels of acolor image, which can be directly extracted using computer software.The HSI model can be converted from the RGB model.

According to the previous work [8], wear debris classificationwas achieved based on the combined hue and intensity compo-nents of HSI color space. Hence, this study adopted the indexes ofhue and intensity to distinguish oxide wear debris. The hue andintensity components can be transformed from R, G and B compo-nents as follows [8]:

H¼ θ ; if BrG

360�θ ; if B4G

(ð3Þ

I¼ 13ðRþGþBÞ ð4Þ

Fig. 9. The training result of the identification model with the data in Table 1.

Fig. 10. Six typical wear debris images captured in the four-ball test monitoring: (a), (b), (c), (d), (e) and (f).

Fig. 11. Image enhancement by applying the segmentation and restoration methods to the particles: (a) the extracted wear debris from Fig. 10 and (b) the improved particleimages of (a).

Y. Peng et al. / Wear 332-333 (2015) 1151–1157 1155

where,

θ¼ arccos1=2 ðR�GÞþðR�BÞ½ �

ðR�GÞ2þðR�BÞðG�BÞh i1=2

8><>:

9>=>; ð5Þ

where H, S and I are the three components of the HSI model. Thevalue ranges of R, G and B components are normalized between0 and 1 here.

The extraction results of the HSI components of the wear debrisin Fig. 8 are shown in Table 1. It can be seen from the table that,based on the indexes of the hue and intensity, the markedparticles were categorized into three groups named as black, redor other. Particles No. 7–9 were firstly identified to be red becausethey had smaller hue values than the others. Second, the blackparticles of No. 4–6 were separated because their intensities wereless than those of particles No. 1–3.

4. Multi-class classification of oxide particles based on supportvector data description

Two typical types of oxidation wear products including redoxide (Fe2O3) and black oxide (Fe3O4) are concerned in this study.For on-line monitoring, a dynamic clustering model of oxide weardebris identification was established with a multi-class classifierbased on support vector data description (Multi-SVDD) [21,22].

4.1. Oxide wear debris identification model based on Multi-SVDD

In this study, all particles were categorized into one of the threegroups, that is, Fe2O3, Fe3O4 and other, based on their indexes of Hand I. A large amount of the data (H, I) of each group were clusteredby SVDD to find the boundaries of the three sets. A newly capturedparticle was classified automatically through calculating the short-est distance between the particle and the boundaries. To train theclassification model, the samples in Table 1 were used as training

data. The hue and intensity values in Table 1 were normalized using

H0 ið Þ ¼ H ið Þ� min Hð Þmax Hð Þ� min Hð Þ; i¼ 1; 2; :::; n ð6Þ

I0 ið Þ ¼ I ið Þ� min Ið Þmax Ið Þ� min Ið Þ; i¼ 1; 2; :::;n ð7Þ

where, H0 ið Þ and I0 ið Þ are the normalized values of hue and intensity;H ið Þ and I ið Þ are the original values of hue and intensity; max :ð Þ andmin :ð Þ are the maximum and minimum of all values, respectively; nis the number of all particles.

Fig. 9 shows the training outcome using the data of the particleslisted in Table 1. As can be seen, the training model was correctlyestablished using the Multi-SVDD approach for oxide particle classi-fication. Although the identification model shows a satisfied result,more particle samples in other colors need to be used to update themodel so it can identify a wider range of oxide wear debris. Bytraining the model with more particles, the identification results willbe more reliable.

4.2. Application of oxide wear debris identification

For on-line oxidation wear monitoring, an application of theidentification model was carried out on the four-ball on-linemonitoring test rig shown in Fig. 1. A 4 h wear test was conductedat a speed of 1000 r/min and a load of 1500 N. The digital pumpdrained the lubricant with a flow rate of 1 mL/min. Videos of dynamicwear debris were sampled at a sampling rate of 50 fps, and the imageswere presented in a true color format with a resolution of 640�480pixels.

Fig. 10 shows six typical images of wear debris generated in thetest. It can be seen that the particles had different colors and thebackground also varied in color. The extracted particles from theimages in Fig. 10 are displayed in Fig. 11(a). As can be seen, theoriginal images are fuzzy due to the particle motion. By applyingthe color image segmentation and enhancement techniques describedin Sections 3.1 and 3.2, the quality of the final wear debris imagesshown in Fig. 11(b) was improved significantly.

The hue and intensity values of the wear debris were extractedfor particle identification. The identification results using the trainedmodel are given in Table 2. Fe2O3, Fe3O4 and other particles weremarked to be “1”, “2” and “3” respectively. The results reveal thatoxide wear debris was produced during the test, which indicates theoccurrence of oxidation wear.

Fig. 12. Images of the worn steel ball: (a) image of the ball and (b) enlarged image of the worn surface.

Table 2The values of H and I of the wear debris in Fig. 11.

Particles No. 1 No. 2 No. 3 No. 4 No. 5 No. 6 No. 7 No. 8

H /degree 0.81 0.21 0.11 0.81 0.65 0.68 0.41 0.64I /dimensionless 0.30 0.32 0.29 0.25 0.23 0.05 0.28 0.11Particle type 3 1 1 3 2 2 3 2

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To further verify the identification results, the tested steel ballswere checked and the wear condition is shown in Fig. 12. As can beseen from Fig. 12(b), the radius of the wear scar is nearly 1.25 mm,and the color around the wear scar is darker than other areas. Thisis because a large amount of heat was generated at the metal–metal contacts during the sliding motion. Metallic oxidationoccurred under high temperature turning the material into a blackcolor. At the same time, the oxidation surface of the friction pairwas worn away during wear. Thereafter, oxidation continued andlocal oxidation spot appeared.

5. Conclusions

Aiming at monitoring oxidation wear in a running machine, anon-line oxide wear debris identification method was establishedby focusing on the color feature extraction of dynamic wearparticles. The color extraction from a blurred image of movingwear debris was studied to characterize different types of oxidewear debris. The following conclusions can be drawn.

1) A video-based imaging system was adopted to acquire real-timecolor images of moving particles. To solve the blurring problemassociated with these on-line images, the image segmentationand restoration methods were proposed to improve the imagequality.

2) The hue and intensity components of the HSI model wereutilized for oxide wear debris classification. By using a Multi-SVDD classifier, two typical types of wear particles, red Fe2O3

and black Fe3O4, were identified.3) Experiment results proved that real-time oxidation wear mon-

itoring can be realized using the method of color extractiondeveloped in this work. This development is potentially usefulfor industry applications.

Acknowledgments

The financial support of the present research was provided bythe National Science Foundation of China (Grant nos. 50905135,51275381) and the Science and Technology Planning Project ofShaanxi Province, China (Grant no. 2012GY2-37). Special thanks tothe financial support from the China Scholarship Council (Grantno. 201406280050) during the term of this project.

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